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We evaluate the uncertainties of methane optimal estimation retrievals from single-footprint thermal infrared observations from the Atmospheric Infrared Sounder (AIRS). These retrievals are primarily sensitive to atmospheric methane in the mid-troposphere through the lower stratosphere (∼2 to ∼17 km). We compare them to in situ observations made from aircraft during the HIAPER Pole to Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) campaigns, and from the NOAA GML aircraft network, between the surface and 5–13 km, across a range of years, latitudes between 60∘ S to 80∘ N, and over land and ocean. After a global, pressure-dependent bias correction, we find that the land and ocean have similar biases and that the reported observation error (combined measurement and interference errors) of ∼27 ppb is consistent with the SD between aircraft and individual AIRS observations. A single observation has measurement (noise related) uncertainty of ∼17 ppb, a ∼20 ppb uncertainty from radiative interferences (e.g., from water or temperature), and ∼30 ppb due to “smoothing error”, which is partially removed when making comparisons to in situ measurements or models in a way that accounts for this regularization. We estimate a 10 ppb validation uncertainty because the aircraft typically did not measure methane at altitudes where the AIRS measurements have some sensitivity, e.g., the stratosphere, and there is uncertainty in the truth that we validate against. Daily averaging only partly reduces the difference between aircraft and satellite observation, likely because of correlated errors introduced into the retrieval from temperature and water vapor. For example, averaging nine observations only reduces the aircraft–model difference to ∼17 ppb vs. the expected ∼10 ppb. Seasonal averages can reduce this ∼17 ppb uncertainty further to ∼10 ppb, as determined through comparison with NOAA aircraft, likely because uncertainties related to radiative effects of temperature and water vapor are reduced when averaged over a season.

In this paper we evaluate new retrievals of the deuterium content of water vapor from the Aqua Atmospheric InfraRed Sounder (AIRS), with aircraft measurements of HDO and H2O from the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES) field mission. Single-footprint AIRS radiances are processed with an optimal estimation algorithm that provides vertical profiles of the HDO∕H2O ratio, characterized uncertainties and instrument operators (i.e., averaging kernel matrix). These retrievals are compared to vertical profiles of the HDO∕H2O ratio from the Oregon State University Water Isotope Spectrometer for Precipitation and Entrainment Research (WISPER) on the ORACLES NASA P-3B Orion aircraft. Measurements were taken over the southeastern Atlantic Ocean from 31 August to 25 September 2016. HDO∕H2O is commonly reported in δD notation, which is the fractional deviation of the HDO∕H2O ratio from the standard reference ratio. For collocated measurements, the satellite instrument operator (averaging kernels and a priori constraint) is applied to the aircraft profile measurements. We find that AIRS δD bias relative to the aircraft is well within the estimated measurement uncertainty. In the lower troposphere, 1000 to 800 hPa, AIRS δD bias is −6.6 ‰ and the root-mean-square (rms) deviation is 20.9 ‰, consistent with the calculated uncertainty of 19.1 ‰. In the mid-troposphere, 800 to 500 hPa, AIRS δD bias is −6.8 ‰ and rms 44.9 ‰, comparable to the calculated uncertainty of 25.8 ‰.

We present an overview of an optimal estimation algorithm to retrieve peroxyacetyl nitrate (PAN) from single-field-of-view Level 1B radiances measured by the Cross-Track Infrared Sounder (CrIS). CrIS PAN retrievals show peak sensitivity in the mid-troposphere, with degrees of freedom for signal less than or equal to 1.0. We show comparisons with two sets of aircraft measurements from the Atmospheric Tomography Mission (ATom), the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) and the Georgia Tech chemical ionization mass spectrometer (GT-CIMS). We find a systematic difference between the two aircraft datasets, with vertically averaged mid-tropospheric values from the GT-CIMS around 14 % lower than equivalent values from PANTHER. However, the two sets of aircraft measurements are strongly correlated (R2 value of 0.92) and do provide a consistent view of the large-scale variation of PAN. We demonstrate that the retrievals of PAN from CrIS show skill in measurement of these large-scale PAN distributions in the remote mid-troposphere compared to the retrieval prior. The standard deviation of individual CrIS–aircraft differences is 0.08 ppbv, which we take as an estimate of the uncertainty of the CrIS mid-tropospheric PAN for a single satellite field of view. The standard deviation of the CrIS–aircraft comparisons for averaged CrIS retrievals (median of 20 satellite coincidences with each aircraft profile) is lower at 0.05 ppbv. This would suggest that the retrieval error is reduced with averaging, although not with the square root of the number of observations. We find a negative bias of the order of 0.1 ppbv in the CrIS PAN results with respect to the aircraft measurements. This bias shows a dependence on column water vapor. We provide a water-vapor-dependent bias correction for use with the CrIS PAN data.

Global multiconstituent concentration and emission fields obtained from the assimilation of the satellite retrievals of ozone, CO, NO2, HNO3, and SO2 from the Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment 2, Measurements of Pollution in the Troposphere, Microwave Limb Sounder, and Atmospheric Infrared Sounder (AIRS)/OMI are used to understand the processes controlling air pollution during the Korea‐United States Air Quality (KORUS‐AQ) campaign. Estimated emissions in South Korea were 0.42 Tg N for NOx and 1.1 Tg CO for CO, which were 40% and 83% higher, respectively, than the a priori bottom‐up inventories, and increased mean ozone concentration by up to 7.5 ± 1.6 ppbv. The observed boundary layer ozone exceeded 90 ppbv over Seoul under stagnant phases, whereas it was approximately 60 ppbv during dynamical conditions given equivalent emissions. Chemical reanalysis showed that mean ozone concentration was persistently higher over Seoul (75.10 ± 7.6 ppbv) than the broader KORUS‐AQ domain (70.5 ± 9.2 ppbv) at 700 hPa. Large bias reductions (>75%) in the free tropospheric OH show that multiple‐species assimilation is critical for balanced tropospheric chemistry analysis and emissions. The assimilation performance was dependent on the particular phase. While the evaluation of data assimilation fields shows an improved agreement with aircraft measurements in ozone (to less than 5 ppbv biases), CO, NO2, SO2, PAN, and OH profiles, lower tropospheric ozone analysis error was largest at stagnant conditions, whereas the model errors were mostly removed by data assimilation under dynamic weather conditions. Assimilation of new AIRS/OMI ozone profiles allowed for additional error reductions, especially under dynamic weather conditions. Our results show the important balance of dynamics and emissions both on pollution and the chemical assimilation system performance., Key Points Multiconstituent data assimilation during KORUS‐AQ showed that emissions in South Korea were 0.42 Tg N for NOx and 1.1 Tg CO for COThese emissions were 40% and 83% higher, respectively, than the a priori bottom‐up inventories and increased ozone by up to 7.5 ± 1.6 ppbvMean ozone concentration was persistently higher over Seoul (75.1 ± 7.6 ppbv) than the broader KORUS‐AQ domain (70.5 ± 9.2 ppbv) at 700 hPa

Following past studies to quantify decadal trends in global carbon monoxide (CO) using satellite observations, we update estimates and find a CO trend in column amounts of about −0.50 % per year between 2002 to 2018, which is a deceleration compared to analyses performed on shorter records that found −1 % per year. Aerosols are co-emitted with CO from both fires and anthropogenic sources but with a shorter lifetime than CO. A combined trend analysis of CO and aerosol optical depth (AOD) measurements from space helps to diagnose the drivers of regional differences in the CO trend. We use the long-term records of CO from the Measurements of Pollution in the Troposphere (MOPITT) and AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Other satellite instruments measuring CO in the thermal infrared, AIRS, TES, IASI, and CrIS, show consistent hemispheric CO variability and corroborate results from the trend analysis performed with MOPITT CO. Trends are examined by hemisphere and in regions for 2002 to 2018, with uncertainties quantified. The CO and AOD records are split into two sub-periods (2002 to 2010 and 2010 to 2018) in order to assess trend changes over the 16 years. We focus on four major population centers: Northeast China, North India, Europe, and Eastern USA, as well as fire-prone regions in both hemispheres. In general, CO declines faster in the first half of the record compared to the second half, while AOD trends show more variability across regions. We find evidence of the atmospheric impact of air quality management policies. The large decline in CO found over Northeast China is initially associated with an improvement in combustion efficiency, with subsequent additional air quality improvements from 2010 onwards. Industrial regions with minimal emission control measures such as North India become more globally relevant as the global CO trend weakens. We also examine the CO trends in monthly percentile values to understand seasonal implications and find that local changes in biomass burning are sufficiently strong to counteract the global downward trend in atmospheric CO, particularly in late summer., SCOPUS: ar.j, info:eu-repo/semantics/published

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6 µm ozone band is a fundamental quantity for understanding chemistry–climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present-day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6 µm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) simulations. The principal culprits are tropical middle and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mW m−2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mW m−2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mW m−2). We found that the atmospheric component (AM3) of the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model and Canadian Middle Atmosphere Model (CMAM) have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to different processes. Overall, the multi-model ensemble mean bias is −133±98 mW m−2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation., Atmospheric Chemistry and Physics, 20 (1), ISSN:1680-7375, ISSN:1680-7367

Peroxyacyl nitrate (PAN) is a critical atmospheric reservoir for nitrogen oxide radicals, and plays a lead role in their redistribution in the troposphere. We analyze new Tropospheric Emission Spectrometer (TES) PAN observations over North America from July 2006 to July 2009. Using aircraft observations from the Colorado Front Range, we demonstrate that TES can be sensitive to elevated PAN in the boundary layer (∼ 750 hPa) even in the presence of clouds. In situ observations have shown that wildfire emissions can rapidly produce PAN, and PAN decomposition is an important component of ozone production in smoke plumes. We identify smoke-impacted TES PAN retrievals by co-location with NOAA Hazard Mapping System (HMS) smoke plumes. Depending on the year, 15–32 % of cases where elevated PAN is identified in TES observations (retrievals with degrees of freedom (DOF) > 0.6) overlap smoke plumes during July. Of all the retrievals attempted in the July 2006 to July 2009 study period, 18 % is associated with smoke . A case study of smoke transport in July 2007 illustrates that PAN enhancements associated with HMS smoke plumes can be connected to fire complexes, providing evidence that TES is sufficiently sensitive to measure elevated PAN several days downwind of major fires. Using a subset of retrievals with TES 510 hPa carbon monoxide (CO) > 150 ppbv, and multiple estimates of background PAN, we calculate enhancement ratios for tropospheric average PAN relative to CO in smoke-impacted retrievals. Most of the TES-based enhancement ratios fall within the range calculated from in situ measurements.

The Aura Tropospheric Emission Spectrometer (TES) is collecting closely spaced observations over 19 megacities. The objective is to obtain measurements that will lead to better understanding of the processes affecting air quality in and around these cities, and to better estimates of the seasonal and interannual variability. We explore the TES measurements of ozone, ammonia, methanol and formic acid collected around the Mexico City metropolitan area (MCMA) and in the vicinity of Lagos (Nigeria). The TES data exhibit seasonal signals that are correlated with Atmospheric Infrared Sounder (AIRS) CO and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), with in situ measurements in the MCMA and with Goddard Earth Observing System (GEOS)-Chem model output in the Lagos area. TES was able to detect an extreme pollution event in the MCMA on 9 April 2013, which is also evident in the in situ data. TES data also show that biomass burning has a greater impact south of the city than in the caldera where Mexico City is located. TES measured enhanced values of the four species over the Gulf of Guinea south of Lagos. Since it observes many cities from the same platform with the same instrument and applies the same retrieval algorithms, TES data provide a very useful tool for easily comparing air quality measures of two or more cities. We compare the data from the MCMA and Lagos, and show that, while the MCMA has occasional extreme pollution events, Lagos consistently has higher levels of these trace gases.

We present two new products from near-infrared Greenhouse Gases Observing Satellite (GOSAT) observations: lowermost tropospheric (LMT, from 0 to 2.5 km) and upper tropospheric–stratospheric (U, above 2.5 km) carbon dioxide partial column mixing ratios. We compare these new products to aircraft profiles and remote surface flask measurements and find that the seasonal and year-to-year variations in the new partial column mixing ratios significantly improve upon the Atmospheric CO2 Observations from Space (ACOS) and GOSAT (ACOS-GOSAT) initial guess and/or a priori, with distinct patterns in the LMT and U seasonal cycles that match validation data. For land monthly averages, we find errors of 1.9, 0.7, and 0.8 ppm for retrieved GOSAT LMT, U, and XCO2; for ocean monthly averages, we find errors of 0.7, 0.5, and 0.5 ppm for retrieved GOSAT LMT, U, and XCO2. In the southern hemispheric biomass burning season, the new partial columns show similar patterns to MODIS fire maps and MOPITT multispectral CO for both vertical levels, despite a flat ACOS-GOSAT prior, and a CO–CO2 emission factor comparable to published values. The difference of LMT and U, useful for evaluation of model transport error, has also been validated with a monthly average error of 0.8 (1.4) ppm for ocean (land). LMT is more locally influenced than U, meaning that local fluxes can now be better separated from CO2 transported from far away.

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

Satellite CO_2 retrievals from the Greenhouse gases Observing SATellite (GOSAT), Atmospheric Infrared Sounder (AIRS), and Tropospheric Emission Spectrometer (TES) and in situ measurements from the National Oceanic and Atmospheric Administration - Earth System Research Laboratory (NOAA-ESRL) Surface CO_2 and Total Carbon Column Observing Network (TCCON) are utilized to explore the CO_2 variability at different altitudes. A multiple regression method is used to calculate the CO_2 annual cycle and semiannual cycle amplitudes from different data sets. The CO_2 annual cycle and semiannual cycle amplitudes for GOSAT X_(CO2) and TCCON X_(CO2) are consistent but smaller than those seen in the NOAA-ESRL surface data. The CO_2 annual and semiannual cycles are smallest in the AIRS midtropospheric CO_2 compared with other data sets in the Northern Hemisphere. The amplitudes for the CO_2 annual cycle and semiannual cycle from GOSAT, TES, and AIRS CO_2 are small and comparable to each other in the Southern Hemisphere. Similar regression analysis is applied to the Model for OZone And Related chemical Tracers-2 and CarbonTracker model CO_2. The convolved model CO_2 annual cycle and semiannual cycle amplitudes are similar to those from the satellite CO_2 retrievals, although the models tend to underestimate the CO_2 seasonal cycle amplitudes in the Northern Hemisphere midlatitudes and underestimate the CO_2 semiannual cycle amplitudes in the high latitudes. These results can be used to better understand the vertical structures for the CO_2 annual cycle and semiannual cycle and help identify deficiencies in the models, which are very important for the carbon budget study.

Single-pixel tropospheric retrievals of HDO and H2O concentrations are retrieved from Atmospheric Infrared Sounder (AIRS) radiances using the optimal estimation algorithm developed for the Aura Tropospheric Emission Spectrometer (TES) project. We evaluate the error characteristics and vertical sensitivity of AIRS measurements corresponding to 5 d of TES data (or five global surveys) during the Northern Hemisphere summers between 2006 and 2010 (∼600 co-located comparisons per day). We find that the retrieval characteristics of the AIRS deuterium content measurements have similar vertical resolution in the middle troposphere as TES but with slightly less sensitivity in the lowermost troposphere, with a typical degrees of freedom (DOFS) in the tropics of 1.5. The calculated measurement uncertainty is ∼30 ‰ (parts per thousand relative to the deuterium composition of ocean water) for a tropospheric average between 750 and 350 hPa, the altitude region where AIRS is most sensitive, compared to ∼15 ‰ for the TES data. Comparison with the TES data also indicates that the uncertainty of a single target AIRS HDO ∕ H2O measurement is ∼30 ‰. Comparison of AIRS and TES data between 30∘ S and 50∘ N indicates that the AIRS data are biased low by ∼-2.6 ‰ with a latitudinal variation of ∼7.8 ‰. This latitudinal variation is consistent with the accuracy of TES data compared to in situ measurements, suggesting that both AIRS and TES have similar accuracy.

A current obstacle to the observation system simulation experiments (OSSEs) used to quantify the potential performance of future atmospheric composition remote sensing systems is a computationally efficient method to define the scene-dependent vertical sensitivity of measurements as expressed by the retrieval averaging kernels (AKs). We present a method for the efficient prediction of AKs for multispectral retrievals of carbon monoxide (CO) and ozone (O3) based on actual retrievals from MOPITT (Measurements Of Pollution In The Troposphere) on the Earth Observing System (EOS)-Terra satellite and TES (Tropospheric Emission Spectrometer) and OMI (Ozone Monitoring Instrument) on EOS-Aura, respectively. This employs a multiple regression approach for deriving scene-dependent AKs using predictors based on state parameters such as the thermal contrast between the surface and lower atmospheric layers, trace gas volume mixing ratios (VMRs), solar zenith angle, water vapor amount, etc. We first compute the singular value decomposition (SVD) for individual cloud-free AKs and retain the first three ranked singular vectors in order to fit the most significant orthogonal components of the AK in the subsequent multiple regression on a training set of retrieval cases. The resulting fit coefficients are applied to the predictors from a different test set of test retrievals cased to reconstruct predicted AKs, which can then be evaluated against the true retrieval AKs from the test set. By comparing the VMR profile adjustment resulting from the use of the predicted vs. true AKs, we quantify the CO and O3 VMR profile errors associated with the use of the predicted AKs compared to the true AKs that might be obtained from a computationally expensive full retrieval calculation as part of an OSSE. Similarly, we estimate the errors in CO and O3 VMRs from using a single regional average AK to represent all retrievals, which has been a common approximation in chemical OSSEs performed to date. For both CO and O3 in the lower troposphere, we find a significant reduction in error when using the predicted AKs as compared to a single average AK. This study examined data from the continental United States (CONUS) for 2006, but the approach could be applied to other regions and times.

Peroxyacetyl nitrate (PAN) is a critical atmospheric reservoir for nitrogen oxide radicals, and it plays a lead role in their redistribution in the troposphere. We analyze new Tropospheric Emission Spectrometer (TES) PAN observations over North America during July 2006 to 2009. Using aircraft observations from the Colorado Front Range, we demonstrate that TES can be sensitive to elevated PAN in the boundary layer even in the presence of clouds. In situ observations have shown that wildfire emissions can rapidly produce PAN, and PAN decomposition is an important component of ozone production in smoke plumes. We identify smoke-impacted TES PAN retrievals by co-location with NOAA Hazard Mapping System (HMS) smoke plumes. We find that 15–32 % of cases where elevated PAN is identified in TES observations (retrievals with DOF > 0.6) overlap smoke plumes. A case study of smoke transport in July 2007 illustrates that PAN enhancements associated with HMS smoke plumes can be connected to fire complexes, providing evidence that TES is sufficiently sensitive to measure elevated PAN several days downwind of major fires. Using a subset of retrievals with TES 510 hPa carbon monoxide (CO) > 150 ppbv, and multiple estimates of background PAN, we calculate enhancement ratios for tropospheric average PAN relative to CO in smoke-impacted retrievals. Most of the TES-based enhancement ratios fall within the range calculated from in situ measurements.

The AURA TES instrument is collecting closely spaced observations over 19 megacities. The objective is to obtain measurements that will lead to better understanding of the processes affecting air quality in and around these cities, and better estimates of the seasonal and interannual variability. We explore the TES measurements of ozone, ammonia, methanol and formic acid collected around the Mexico City Metropolitan Area and in the vicinity of Lagos (Nigeria). The TES data exhibit seasonal signals that are correlated with AIRS CO and MODIS AOD, with in situ measurements in the MCMA and with GEOS-Chem model output in the Lagos area. TES was able to detect an extreme pollution event in the MCMA on April 9, 2013, which is also evident in the in situ data. TES also shows that biomass burning has a greater impact south of the city than in the caldera where Mexico City is located. TES measured enhanced values of the four species over the Gulf of Guinea south of Lagos. Since it observes many cities from the same platform, with the same instrument and applyies the same retrieval algorithms, TES data provide a very useful tool for quickly comparing air quality measures of two or more cities. We compare the data from the MCMA and Lagos, and show that while the MCMA has occasional extreme pollution events, Lagos consistently has far higher levels of these trace gases.

The EOS (Earth Observing System) Aura Tropospheric Emission Spectrometer (TES) retrieves the atmospheric HDO / H2O ratio in the mid-to-lower troposphere as well as the planetary boundary layer. TES observations of water vapor and the HDO isotopologue have been compared with nearly coincident in situ airborne measurements for direct validation of the TES products. The field measurements were made with a commercially available Picarro L1115-i isotopic water analyzer on aircraft over the Alaskan interior boreal forest during the three summers of 2011 to 2013. TES special observations were utilized in these comparisons. The TES averaging kernels and a priori constraints have been applied to the in situ data, using version 5 (V005) of the TES data. TES calculated errors are compared with the standard deviation (1σ) of scan-to-scan variability to check consistency with the TES observation error. Spatial and temporal variations are assessed from the in situ aircraft measurements. It is found that the standard deviation of scan-to-scan variability of TES δD is ±34.1‰ in the boundary layer and ± 26.5‰ in the free troposphere. This scan-to-scan variability is consistent with the TES estimated error (observation error) of 10–18‰ after accounting for the atmospheric variations along the TES track of ±16‰ in the boundary layer, increasing to ±30‰ in the free troposphere observed by the aircraft in situ measurements. We estimate that TES V005 δD is biased high by an amount that decreases with pressure: approximately +123‰ at 1000 hPa, +98‰ in the boundary layer and +37‰ in the free troposphere. The uncertainty in this bias estimate is ±20‰. A correction for this bias has been applied to the TES HDO Lite Product data set. After bias correction, we show that TES has accurate sensitivity to water vapor isotopologues in the boundary layer.

We present a description of the NASA Aura Tropospheric Emission Spectrometer (TES) carbonyl sulfide (OCS) retrieval algorithm for oceanic observations, along with evaluation of the biases and uncertainties using aircraft profiles from the HIPPO (HIAPER Pole-to-Pole Observations) campaign and data from the NOAA Mauna Loa site. In general, the OCS retrievals (1) have less than 1.0 degree of freedom for signals (DOFs), (2) are sensitive in the mid-troposphere with a peak sensitivity typically between 300 and 500 hPa, (3) but have much smaller systematic errors from temperature, CO2 and H2O calibrations relative to random errors from measurement noise. We estimate the monthly means from TES measurements averaged over multiple years so that random errors are reduced and useful information about OCS seasonal and latitudinal variability can be derived. With this averaging, TES OCS data are found to be consistent (within the calculated uncertainties) with NOAA ground observations and HIPPO aircraft measurements. TES OCS data also captures the seasonal and latitudinal variations observed by these in situ data.

Peroxyacetyl nitrate (PAN) plays a fundamental role in the global ozone budget and is the primary reservoir of tropospheric reactive nitrogen over much of the globe. However, large uncertainties exist in how surface emissions, transport and lightning affect the global distribution, particularly in the tropics. We present new satellite observations of free tropospheric PAN in the tropics from the Aura Tropospheric Emission Spectrometer. This dataset allows us to test expected spatio-temporal distributions that have been predicted by models but previously not well observed. We compare here with the GEOS-Chem model with updates specifically for PAN. We observe an austral springtime maximum over the tropical Atlantic, a feature that model predictions attribute primarily to lightning. Over Northern Central Africa in December, observations show strong inter-annual variability, despite low variation in fire emissions, that we attribute to the combined effects of changes in biogenic emissions and lightning. We observe small enhancements in free tropospheric PAN corresponding to the extreme burning event over Indonesia associated with the 2006 El Nino.

[1] TES (Tropospheric Emission Spectrometer) nadir and MLS (Microwave Limb Sounder) limb measurements from the Aura satellite are used to jointly estimate an atmospheric carbon monoxide (CO) profile with extended vertical range compared to profiles retrieved from the individual measurement. We describe the algorithms, the processing procedures, the prototyping results, and the evaluations for this new joint product. TES and MLS “stand-alone” CO profile retrievals are largely complementary, with TES being largely sensitive to lower to middle troposphere while MLS measures CO in the upper troposphere and above. We pair TES nadir and MLS limb tangent locations within 6–8 min and within 220 km. The paired radiance measurements of the two instruments in each location are optimally combined to retrieve a single CO profile along with other trace gases whose signal interferes with that from CO. This combined CO profile has a vertical resolution and vertical range that is an improvement over the two stand-alone products, especially in the upper troposphere/lower stratosphere. For example, the degrees of freedom for signal (DOFS) between surface and 50 hPa for TES alone are

[1] Dry conditions from a moderate El Nino during the fall of 2006 resulted in enhanced burning in Indonesia with fire emissions of CO approximately 4–6 times larger than the prior year. Here we use new tropospheric methane and CO data from the Aura Tropospheric Emission Spectrometer and new CO profile measurements from the Terra Measurements of Pollution in the Troposphere (MOPITT) satellite instruments with the Goddard Earth Observing System (GEOS)-Chem model to estimate methane emissions of 4.25 ± 0.75 Tg for October–November 2006 from these fires. Errors in convective parameterization in GEOS-Chem, evaluated by comparing MOPITT and GEOS-Chem CO profiles, are the primary uncertainty of the emissions estimate. The El Nino related Indonesian fires increased the tropical distribution of atmospheric methane relative to 2005, indicating that tropical biomass burning can compensate for expected decreases in tropical wetland methane emissions from reduced rainfall during El Nino as found in previous studies.

Tropical fires represent a highly uncertain source of atmospheric methane (CH4) because of the variability of fire emissions and the dependency of the fire CH4 emission factors (g kg−1 dry matter burned) on fuel type and combustion phase. In this paper we use new observations of CH4 and CO in the free troposphere from the Aura Tropospheric Emission Sounder (TES) satellite instrument to place constraints on the role of tropical fire emissions versus microbial production (e.g. in wetlands and livestock) during the (October) 2006 El Niño, a time of significant fire emissions from Indonesia. We first compare the global CH4 distributions from TES using the GEOS-Chem model. We find a mean bias between the observations and model of 26.3 ppb CH4 that is independent of latitude between 50° S and 80° N, consistent with previous validation studies of TES CH4 retrievals using aircraft measurements. The slope of the distribution of CH4 versus CO as observed by TES and modeled by GEOS-Chem is consistent (within the TES observation error) for air parcels over the Indonesian peat fires, South America, and Africa. The CH4 and CO distributions are correlated between R = 0.42 and R = 0.46, with these correlations primarily limited by the TES random error. Over Indonesia, the observed slope of 0.13 (ppb ppb−1) ±0.01, as compared to a modeled slope of 0.153 (ppb ppb−1) ±0.005 and an emission ratio used within the GEOS-Chem model of approximately 0.11 (ppb ppb−1), indicates that most of the observed methane enhancement originated from the fire. Slopes of 0.47 (ppb ppb−1) ±0.04 and 0.44 (ppb ppb−1) ±0.03 over South America and Africa show that the methane in the observed air parcels primarily came from microbial-generated emissions. Sensitivity studies using GEOS-Chem show that part of the observed correlation for the Indonesian observations and most of the observed correlations over South America and Africa are a result of transport and mixing of the fire and nearby microbial-generated emissions into the observed air parcels. Differences between observed and modeled CH4 distributions over South America and southern Africa indicate that the magnitude of the methane emissions for this time period are inconsistent with observations even if the relative distribution of fire versus biotic emissions are consistent. This study shows the potential for estimation of CH4 emissions over tropical regions using joint satellite observations of CH4 and CO.

We present satellite based ozone profile estimates derived by combining radiances measured at thermal infrared (TIR) wavelengths from the Aura Tropospheric Emission Spectrometer (TES) and ultraviolet (UV) wavelengths measured by the Aura Ozone Monitoring Instrument (OMI). The advantage of using these combined wavelengths and instruments for sounding ozone over either instrument alone is improved sensitivity near the surface as well as the capability to consistently resolve the lower troposphere, upper troposphere, and lower stratosphere for scenes with varying geophysical states. For example, the vertical resolution of ozone estimates from either TES or OMI varies strongly by surface albedo and temperature. Typically, TES provides 1.6 degrees of freedom for signal (DOFS) and OMI provides less than 1 DOFS in the troposphere. The combination provides 2 DOFS in the troposphere with approximately 0.4 DOFS for near surface ozone (surface to 700 hPa). We evaluated these new ozone profile estimates with ozonesonde measurements and found that calculated errors for the joint TES and OMI ozone profile estimates are in reasonable agreement with actual errors as derived by the root-mean-square (RMS) difference between the ozonesondes and the joint TES/OMI ozone estimates. We also used a common a priori profile in the retrievals in order to evaluate the capability of different retrieval approaches on capturing near-surface ozone variability. We found that the vertical resolution of the joint TES/OMI ozone profile estimates shows significant improvements on quantifying variations in near-surface ozone with RMS differences of 49.9% and correlation coefficient of R = 0.58 for the TES/OMI near-surface estimates as compared to 67.2% RMS difference and R = 0.33 for TES and 115.8% RMS difference and R = 0.09 for OMI. This comparison removes the impacts of using the climatological a priori in the retrievals. However, it results in artificially large sonde/retrieval differences. The TES/OMI ozone profiles from the production code of joint retrievals will use climatological a priori and therefore will have more realistic ozone estimates than those from using a common a priori volume mixing ratio profile.

Thermal infrared radiances from the Tropospheric Emission Spectrometer (TES) between 10 and 15 μm contain significant carbon dioxide (CO2) information, however the CO2 signal must be separated from radiative interference from temperature, surface and cloud parameters, water, and other trace gases. Validation requires data sources spanning the range of TES CO2 sensitivity, which is approximately 2.5 to 12 km with peak sensitivity at about 5 km and the range of TES observations in latitude (40° S to 40° N) and time (2005–2011). We therefore characterize Tropospheric Emission Spectrometer (TES) CO2 version 5 biases and errors through comparisons to ocean and land-based aircraft profiles and to the CarbonTracker assimilation system. We compare to ocean profiles from the first three Hiaper Pole-to-Pole Observations (HIPPO) campaigns between 40° S and 40° N with measurements between the surface and 14 km and find that TES CO2 estimates capture the seasonal and latitudinal gradients observed by HIPPO CO2 measurements. Actual errors range from 0.8–1.8 ppm, depending on the campaign and pressure level, and are approximately 1.6–2 times larger than the predicted errors. The bias of TES versus HIPPO is within 1 ppm for all pressures and datasets; however, several of the sub-tropical TES CO2 estimates are lower than expected based on the calculated errors. Comparisons to land aircraft profiles from the United States Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) between 2005 and 2011 measured from the surface to 5 km to TES CO2 show good agreement with an overall bias of −0.3 ppm to 0.1 ppm and standard deviations of 0.8 to 1.0 ppm at different pressure levels. Extending the SGP aircraft profiles above 5 km using AIRS or CONTRAIL measurements improves comparisons with TES. Comparisons to CarbonTracker (version CT2011) show a persistent spatially dependent bias pattern and comparisons to SGP show a time-dependent bias of −0.2 ppm yr−1. We also find that the predicted sensitivity of the TES CO2 estimates is too high, which results from using a multi-step retrieval for CO2 and temperature. We find that the averaging kernel in the TES product corrected by a pressure-dependent factor accurately reflects the sensitivity of the TES CO2 product.

Monitoring the global distribution and long-term variations of CO2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO2 concentrations from ground-based near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO2 column amount. Estimates of tropospheric CO2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO2 estimates into the GEOS-Chem model. We find that quantifying lower tropospheric CO2 by subtracting free tropospheric CO2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of −5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO2 with the precision and accuracy needed to quantify lower tropospheric CO2 variability.

We describe the approach for the estimation of the atmospheric state, e.g., temperature, water, ozone, from calibrated, spectral radiances measured from the Tropospheric Emission Spectrometer (TES) onboard the Aura spacecraft. The methodology is based on the maximum a posteriori estimate, which mathematically requires the minimization of the difference between observed spectral radiances and a nonlinear model of radiative transfer of the atmospheric state subject to the constraint that the estimated state must be consistent with an a priori probability distribution for that state. The minimization techniques employed here are based on the trust-region Levenberg-Marquardt algorithm. An analysis of the errors for this estimate include smoothing, random, spectroscopic, "cross-state," representation, and systematic errors. In addition, several metrics and diagnostics are introduced that assess the resolution, quality, and statistical significance of the retrievals. We illustrate this methodology for the retrieval of atmospheric and surface temperature, water vapor, and ozone over the Gulf of Mexico on November 3, 2004. © 2006 IEEE.

Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (XCO2) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 ppm vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to error2 = a2 + b2/n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of ∼ 0.3 ppm, with biases larger than the TCCON-predicted bias uncertainty of 0.4 ppm at many stations. We find that GOSAT and CT2013b underpredict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53° N, MACC overpredicts between 26 and 37° N, and CT2013b underpredicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–50 % the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45–67° N for GOSAT and CT2013b.

Thermal infrared (IR) radiances measured near 8 microns contain information about the vertical distribution of water vapor (H2O), the water isotopologue HDO, and methane (CH4), key gases in the water and carbon cycles. Previous versions (Version 4 or less) of the TES profile retrieval algorithm used a "spectral-window" approach to minimize uncertainty from interfering species at the expense of reduced vertical resolution and sensitivity. In this manuscript we document changes to the vertical resolution and uncertainties of the TES version 5 retrieval algorithm. In this version (Version 5), joint estimates of H2O, HDO, CH4 and nitrous oxide (N2O) are made using radiances from almost the entire spectral region between 1100 cm−1 and 1330 cm−1. The TES retrieval constraints are also modified in order to better use this information. The new H2O estimates show improved vertical resolution in the lower troposphere and boundary layer, while the new HDO/H2O estimates can now profile the HDO/H2O ratio between 925 hPa and 450 hPa in the tropics and during summertime at high latitudes. The new retrievals are now sensitive to methane in the free troposphere between 800 and 150 mb with peak sensitivity near 500 hPa; whereas in previous versions the sensitivity peaked at 200 hPa. However, the upper troposphere methane concentrations are biased high relative to the lower troposphere by approximately 4% on average. This bias is likely related to temperature, calibration, and/or methane spectroscopy errors. This bias can be mitigated by normalizing the CH4 estimate by the ratio of the N2O estimate relative to the N2O prior, under the assumption that the same systematic error affects both the N2O and CH4 estimates. We demonstrate that applying this ratio theoretically reduces the CH4 estimate for non-retrieved parameters that jointly affect both the N2O and CH4 estimates. The relative upper troposphere to lower troposphere bias is approximately 2.8% after this bias correction. Quality flags based upon the vertical variability of the methane and N2O estimates can be used to reduce this bias further. While these new CH4, HDO/H2O, and H2O estimates are consistent with previous TES retrievals in the altitude regions where the sensitivities overlap, future comparisons with independent profile measurement will be required to characterize the biases of these new retrievals and determine if the calculated uncertainties using the new constraints are consistent with actual uncertainties.

One of the important science requirements of the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission is to be able to measure ozone with two degrees of freedom in the troposphere and sensitivity in the lowest 2 km (lowermost troposphere, LMT), in order to characterize air quality and boundary layer transport of pollution. Currently available remote sensing techniques utilize backscattered solar ultraviolet (UV) radiances or thermal infrared (TIR) emissions to perform ozone retrievals. However, in the TIR, measurement sensitivity to the LMT requires high thermal contrast between the Earth’s surface and the near-surface (tens to hundreds of meters above surface) atmosphere, while in the UV, the measurement sensitivity to the LMT is low because of Rayleigh scattering. In this paper, we explore the feasibility of using multi-spectral intensity measurements in the UV, visible (VIS), mid infrared (MIR) and TIR, and polarization measurements in the UV/VIS, to improve tropospheric and lowermost tropospheric ozone retrievals. Simulations for 16 cloud and aerosol free atmospheric profiles spanning a range of ozone mixing ratios indicate that adding VIS measurements to UV measurements significantly enhances the sensitivity to lowermost tropospheric ozone, but only makes a slight improvement to the total degrees of freedom for signal (DFS). On the other hand, the combination of UV and TIR significantly improves the total DFS as well as the lowermost tropospheric DFS. The analysis presented here is a necessary and important first step for defining spectral regions that can meet the GEO-CAPE measurement requirements, and subsequently, the requirements for instrumentation. In this work, the principle of multi-spectral retrievals has been extended from previously published literature and we show that the UV + VIS, UV + TIR and UV + VIS + TIR combinations have the potential to meet the GEO-CAPE measurement requirements for tropospheric ozone. Our analysis includes errors from water and surface properties; further analysis is needed to include temperature, additional gas interferents, clouds, aerosols and more realistic surface properties. These simulations must be run on a much larger dataset, followed by OSSEs (Observing System Simulation Experiments), where simulated retrievals are assimilated into chemical-transport models, to quantitatively assess the impact of the proposed measurements for constraining the spatiotemporal distribution of ozone in the LMT for basic science studies and applications such as air quality forecasts.

We conduct an Observing System Simulation Experiment (OSSE) to test the ability of geostationary satellite measurements of ozone in different spectral regions to constrain surface ozone concentrations through data assimilation. Our purpose is to define instrument requirements for the NASA GEO-CAPE geostationary air quality mission over North America. We consider instruments using different spectral combinations of UV (290–340 nm), Vis (560–620 nm), and thermal IR (TIR, 9.6 μm). Hourly ozone data from the MOZART global 3-D chemical transport model (CTM) are taken as the “true” atmosphere to be sampled by the instruments for July 2001. The resulting synthetic data are assimilated in the GEOS-Chem CTM using a Kalman filter. The MOZART and GEOS-Chem CTMs have independent heritages and use different assimilated meteorological data sets for the same period, making for an objective OSSE. We show that hourly observations of ozone from geostationary orbit improve the assimilation considerably relative to daily observation from low earth orbit, and that broad observation over the ocean is unnecessary if the objective is to constrain surface ozone distribution over land. We also show that there is little propagation of ozone information from the free troposphere to the surface, so that instrument sensitivity in the boundary layer is essential. UV + Vis and UV + TIR spectral combinations improve greatly the information on surface ozone relative to UV alone. UV + TIR is preferable under high-sensitivity conditions with strong thermal contrast at the surface, but UV + Vis is preferable under low-sensitivity conditions. Assimilation of data from a UV + Vis + TIR instrument reduces the GEOS-Chem error for surface ozone by a factor of two. Observation in the TIR is critical to obtain ozone information in the upper troposphere relevant to climate forcing.

The utilization of Tropospheric Emission Spectrometer (TES) Level 2 (L2) retrieval products for the purpose of assessing long term changes in atmospheric trace gas composition requires knowledge of the overall radiometric stability of the Level 1B (L1B) radiances. The purpose of this study is to evaluate the stability of the radiometric calibration of the TES instrument by analyzing the difference between measured and calculated brightness temperatures in selected window regions of the spectrum. The Global Modeling and Assimilation Office (GMAO) profiles for temperature and water vapor and the Real-Time Global Sea Surface Temperature (RTGSST) are used as input to the Optimal Spectral Sampling (OSS) radiative transfer model to calculate the simulated spectra. The TES reference measurements selected cover a 4-year period of time from mid 2005 through mid 2009 with the selection criteria being; observation latitudes greater than −30° and less than 30°, over ocean, Global Survey mode (nadir view) and retrieved cloud optical depth of less than or equal to 0.01. The TES cloud optical depth retrievals are used only for screening purposes and no effects of clouds on the radiances are included in the forward model. This initial screening results in over 55 000 potential reference spectra spanning the four year period. Presented is a trend analysis of the time series of the residuals (observation minus calculations) in the TES 2B1, 1B2, 2A1, and 1A1 bands, with the standard deviation of the residuals being approximately equal to 0.6 K for bands 2B1, 1B2, 2A1, and 0.9 K for band 1A1. The analysis demonstrates that the trend in the residuals is not significantly different from zero over the 4-year period. This is one method used to demonstrate that the relative radiometric calibration is stable over time, which is very important for any longer term analysis of TES retrieved products (L2), particularly well-mixed species such as carbon dioxide and methane.

We infer CO2 surface fluxes using satellite observations of mid-tropospheric CO2 from the Tropospheric Emission Spectrometer (TES) and measurements of CO2 from surface flasks in a time-independent inversion analysis based on the GEOS-Chem model. Using TES CO2 observations over oceans, spanning 40° S–40° N, we find that the horizontal and vertical coverage of the TES and flask data are complementary. This complementarity is demonstrated by combining the datasets in a joint inversion, which provides better constraints than from either dataset alone, when a posteriori CO2 distributions are evaluated against independent ship and aircraft CO2 data. In particular, the joint inversion offers improved constraints in the tropics where surface measurements are sparse, such as the tropical forests of South America. Aggregating the annual surface-to-atmosphere fluxes from the joint inversion for the year 2006 yields −1.13±0.21 Pg C for the global ocean, −2.77±0.20 Pg C for the global land biosphere and −3.90±0.29 Pg C for the total global natural flux (defined as the sum of all biospheric, oceanic, and biomass burning contributions but excluding CO2 emissions from fossil fuel combustion). These global ocean and global land fluxes are shown to be near the median of the broad range of values from other inversion results for 2006. To achieve these results, a bias in TES CO2 in the Southern Hemisphere was assessed and corrected using aircraft flask data, and we demonstrate that our results have low sensitivity to variations in the bias correction approach. Overall, this analysis suggests that future carbon data assimilation systems can benefit by integrating in situ and satellite observations of CO2 and that the vertical information provided by satellite observations of mid-tropospheric CO2 combined with measurements of surface CO2, provides an important additional constraint for flux inversions.

The Aura satellite Tropospheric Emission Spectrometer (TES) instrument is capable of measuring the HDO/H2O ratio in the lower troposphere using thermal infrared radiances between 1200 and 1350 cm−1. However, direct validation of these measurements is challenging due to a lack of in situ measured vertical profiles of the HDO/H2O ratio that are spatially and temporally co-located with the TES observations. From 11 October through 5 November 2008, we undertook a campaign to measure HDO and H2O at the Mauna Loa observatory in Hawaii for comparison with TES observations. The Mauna Loa observatory is situated at 3.1 km above sea level or approximately 680 hPa, which is approximately the altitude where the TES HDO/H2O observations show the most sensitivity. Another advantage of comparing in situ data from this site to estimates derived from thermal IR radiances is that the volcanic rock is heated by sunlight during the day, thus providing significant thermal contrast between the surface and atmosphere; this thermal contrast increases the sensitivity to near surface estimates of tropospheric trace gases. The objective of this inter-comparison is to better characterize a bias in the TES HDO data, which had been previously estimated to be approximately 5 % too high for a column integrated value between 850 hPa and 500 hPa. We estimate that the TES HDO profiles should be corrected downwards by approximately 4.8 % and 6.3 % for Versions 3 and 4 of the data respectively. These corrections must account for the vertical sensitivity of the TES HDO estimates. We estimate that the precision of this bias correction is approximately 1.9 %. The accuracy is driven by the corrections applied to the in situ HDO and H2O measurements using flask data taken during the inter-comparison campaign and is estimated to be less than 1 %. Future comparisons of TES data to accurate vertical profiles of in situ measurements are needed to refine this bias estimate.

The use of global three-dimensional (3-D) models with satellite observations of CO2 in inverse modeling studies is an area of growing importance for understanding Earth's carbon cycle. Here we use the GEOS-Chem model (version 8-02-01) CO2 mode with multiple modifications in order to assess their impact on CO2 forward simulations. Modifications include CO2 surface emissions from shipping (~0.19 Pg C yr−1), 3-D spatially-distributed emissions from aviation (~0.16 Pg C yr−1), and 3-D chemical production of CO2 (~1.05 Pg C yr−1). Although CO2 chemical production from the oxidation of CO, CH4 and other carbon gases is recognized as an important contribution to global CO2, it is typically accounted for by conversion from its precursors at the surface rather than in the free troposphere. We base our model 3-D spatial distribution of CO2 chemical production on monthly-averaged loss rates of CO (a key precursor and intermediate in the oxidation of organic carbon) and apply an associated surface correction for inventories that have counted emissions of CO2 precursors as CO2. We also explore the benefit of assimilating satellite observations of CO into GEOS-Chem to obtain an observation-based estimate of the CO2 chemical source. The CO assimilation corrects for an underestimate of atmospheric CO abundances in the model, resulting in increases of as much as 24% in the chemical source during May–June 2006, and increasing the global annual estimate of CO2 chemical production from 1.05 to 1.18 Pg C. Comparisons of model CO2 with measurements are carried out in order to investigate the spatial and temporal distributions that result when these new sources are added. Inclusion of CO2 emissions from shipping and aviation are shown to increase the global CO2 latitudinal gradient by just over 0.10 ppm (~3%), while the inclusion of CO2 chemical production (and the surface correction) is shown to decrease the latitudinal gradient by about 0.40 ppm (~10%) with a complex spatial structure generally resulting in decreased CO2 over land and increased CO2 over the oceans. Since these CO2 emissions are omitted or misrepresented in most inverse modeling work to date, their implementation in forward simulations should lead to improved inverse modeling estimates of terrestrial biospheric fluxes.

We compare Tropospheric Emission Spectrometer (TES) versions 3 and 4, V003 and V004, respectively, nadir-stare ozone profiles with ozonesonde profiles from the Arctic Intensive Ozonesonde Network Study (ARCIONS, http://croc.gsfc.nasa.gov/arcions/ during the Arctic Research on the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field mission. The ozonesonde data are from launches timed to match Aura's overpass, where 11 coincidences spanned 44° N to 71° N from April to July 2008. Using the TES "stare" observation mode, 32 observations are taken over each coincidental ozonesonde launch. By effectively sampling the same air mass 32 times, comparisons are made between the empirically-calculated random errors to the expected random errors from measurement noise, temperature and interfering species, such as water. This study represents the first validation of high latitude (>70°) TES ozone. We find that the calculated errors are consistent with the actual errors with a similar vertical distribution that varies between 5% and 20% for V003 and V004 TES data. In general, TES ozone profiles are positively biased (by less than 15%) from the surface to the upper-troposphere (~1000 to 100 hPa) and negatively biased (by less than 20%) from the upper-troposphere to the lower-stratosphere (100 to 30 hPa) when compared to the ozonesonde data. Lastly, for V003 and V004 TES data between 44° N and 71° N there is variability in the mean biases (from −14 to +15%), mean theoretical errors (from 6 to 13%), and mean random errors (from 9 to 19%).

We present carbon dioxide (CO2) estimates from the Tropospheric Emission Spectrometer (TES) on the EOS-Aura satellite launched in 2004. For observations between 40° S and 45° N, we find about 1 degree of freedom with peak sensitivity at 511 hPa. The estimated error is ~10 ppm for a single target and 1.3–2.3 ppm for monthly averages on spatial scales of 20°×30°. Monthly spatially-averaged TES data from 2005–2008 processed with a uniform initial guess and prior are compared to CONTRAIL aircraft data over the Pacific ocean, aircraft data at the Southern Great Plains (SGP) ARM site in the southern US, and the Mauna Loa and Samoa surface stations. Comparisons to Mauna Loa data show a correlation of 0.92, a standard deviation of 1.3 ppm, a predicted error of 1.2 ppm, and a ~2% low bias, which is subsequently corrected. Comparisons to SGP aircraft data over land show a correlation of 0.67 and a standard deviation of 2.3 ppm. TES data between 40° S and 45° N for 2006–2007 are compared to surface flask data, GLOBALVIEW, the Atmospheric Infrared Sounder (AIRS), and CarbonTracker. Comparison to GLOBALVIEW-CO2 ocean surface sites shows a correlation of 0.60 which drops when TES is offset in latitude, longitude, or time. At these same locations, TES shows a 0.62 and 0.67 correlation to CarbonTracker at the surface and 5 km, respectively. We also conducted an observing system simulation experiment to assess the potential utility of the TES data for inverse modeling of CO2 fluxes. We find that if biases in the data and model are well characterized, the averaged data have the potential to provide sufficient information to significantly reduce uncertainty on annual estimates of regional CO2 sources and sinks. Averaged pseudo-data at 10°×10° reduced uncertainty in flux estimates by as much as 70% for some tropical regions.

Concurrent tropospheric O3 and CO vertical profiles from the Tropospheric Emission Spectrometer (TES) during the MILAGRO/INTEX-B aircraft campaigns over the Mexico City Metropolitan Area (MCMA) and its surrounding regions were used to examine Mexico City pollution outflow on a regional scale. The pollution outflow from the MCMA occurred predominantly at 600–800 hPa as evident in O3, CO, and NOx enhancements in the in situ aircraft observations. TES O3 and CO are sensitive to the MCMA pollution outflow due to their relatively high sensitivities at 600–800 hPa. We examined O3, CO, and their correlation at 600–800 hPa from TES retrievals, aircraft measurements, and GEOS-Chem model results. TES captures much of the spatial and day-to-day variability of O3 seen in the in situ data. TES CO, however, shows much less spatial and day-to-day variability compared with the in situ observations. The DO3/DCO slope is significantly higher in the TES data (0.43) than the in situ data (0.28) due partly to the lack of variability in TES CO. Extraordinarily high DO3/DCO slope (0.81) from TES observations at 618 hPa over the Eastern U.S. was previously reported by Zhang et al. [Zhang, L., Jacob, D.J., Bowman, K.W., et al., 2006. Ozone–CO correlations determined by the TES satellite instrument in continental outflow regions. Geophys. Res. Lett. 33, L18804. 10.1029/2006GL026399.]. Thus the application of TES CO–O3 correlation to map continental pollution outflow needs further examination. Published by Elsevier Ltd.

Non-linear maximum a posteriori (MAP) estimates of atmospheric profiles from the Tropospheric Emission Spectrometer (TES) contains a priori information that may vary geographically, which is a confounding factor in the analysis and physical interpretation of an ensemble of profiles. One mitigation strategy is to transform profile estimates to a common prior using a linear operation thereby facilitating the interpretation of profile variability. However, this operation is dependent on the assumption of not worse than moderate non-linearity near the solution of the non-linear estimate. The robustness of this assumption is tested by comparing atmospheric retrievals from the Tropospheric Emission Spectrometer processed with a uniform prior with those processed with a variable prior and converted to a uniform prior following the non-linear retrieval. Linearly converting the prior following a non-linear retrieval is shown to have a minor effect on the results as compared to a non-linear retrieval using a uniform prior when compared to the expected total error, with less than 10% of the change in the prior ending up as unbiased fluctuations in the profile estimate results.

Author(s): Kuai, L; Worden, JR; Campbell, JE; Kulawik, SS; Li, KF; Lee, M; Weidner, RJ; Montzka, SA; Moore, FL; Berry, JA; Baker, I; Denning, AS; Bian, H; Bowman, KW; Liu, J; Yung, YL | Abstract: Quantifying the carbonyl sulfide (OCS) land/ocean fluxes contributes to the understanding of both the sulfur and carbon cycles. The primary sources and sinks of OCS are very likely in a steady state because there is no significant observed trend or interannual variability in atmospheric OCS measurements. However, the magnitude and spatial distribution of the dominant ocean source are highly uncertain due to the lack of observations. In particular, estimates of the oceanic fluxes range from approximately 280 Gg S yr-1 to greater than 800 Gg S yr-1, with the larger flux needed to balance a similarly sized terrestrial sink that is inferred from NOAA continental sites. Here we estimate summer tropical oceanic fluxes of OCS in 2006 using a linear flux inversion algorithm and new OCS data acquired by the Aura Tropospheric Emissions Spectrometer (TES). Modeled OCS concentrations based on these updated fluxes are consistent with HIAPER Pole-to-Pole Observations during 4th airborne campaign and improve significantly over the a priori model concentrations. The TES tropical ocean estimate of 70±16GgS in June,when extrapolated over the whole year (about 840 ± 192 Gg S yr-1), supports the hypothesis proposed by Berry et al. (2013) that the ocean flux is in the higher range of approximately 800 Gg S yr-1.

Evaluating surface fluxes of CH4 using total column data requires models to accurately account for the transport and chemistry of methane in the free-troposphere and stratosphere, thus reducing sensitivity to the underlying fluxes. Vertical profiles of methane have increased sensitivity to surface fluxes because lower tropospheric methane is more sensitive to surface fluxes than a total column. Resolving the free troposphere from the lower-troposphere also helps to evaluate the impact of transport and chemistry uncertainties on estimated surface fluxes. Here we demonstrate the potential for estimating lower tropospheric CH4 concentrations through the combination of free-tropospheric methane measurements from the Aura Tropospheric Emission Spectrometer (TES) and XCH4 (dry-mole air fraction of methane) from the Greenhouse Gases Observing Satellite Thermal And Near Infrared for Carbon Observations (GOSAT TANSO, herein GOSAT for brevity). The mean precision of these estimates are calculated to be ~ 23 ppb for a monthly average on a 4 × 5 latitude/longitude degree grid making these data suitable for evaluating lower-tropospheric methane concentrations. Smoothing error is approximately 10 ppb or less. The accuracy is primarily determined by knowledge error of XCO2, used to estimate XCH4 from the GOSAT CH4/CO2 "proxy" retrieval. For example, we use different XCO2 fields to quantify XCH4 from the GOSAT CH4/CO2 retrieval, one from Carbontracker and another from the NASA Carbon Monitoring System, and find that differences of up to approximately 60 ppb are possible with a mean value of approximately 35 ppb or less for any given latitude for these lower-tropospheric methane estimates using these two different XCO2 distributions. We show that these lower-tropospheric concentrations are more directly sensitive to the underlying fluxes than a total column using the GEOS-Chem model. In particular, we compare these lower-tropospheric methane estimates with those from the GEOS-Chem model for July 2009 to determine if these data can capture methane enhancements associated with the high-latitude methane fluxes because both TES and GOSAT separately do not show much sensitivity to methane from these sources. We find that the spatial patterns and magnitude of lower tropospheric methane concentrations from GEOS-Chem over Northern European and Siberian wetland fluxes are consistent with these data but modeled concentrations are much larger than measured over Canadian wetland fluxes. Transport of methane significantly affects lower-tropospheric methane concentrations over S.E. Asia as both data and model show methane enhancements that are shifted away from their sources. A possible new finding is that there is no representation of a strong source between the Black and Caspian seas.

We present a description of the algorithm used to retrieve peroxyacetyl nitrate (PAN) concentrations from the Aura Tropospheric Emission Spectrometer (TES). We describe the spectral microwindows, error analysis, and the utilization of a priori and initial guess information provided by the GEOS-Chem global chemical transport model. The TES PAN retrievals contain up to one degree of freedom for signal. In general, the retrievals are most sensitive to PAN in the mid-troposphere. Estimated single-measurement uncertainties are on the order of 30 to 50%. The detection limit for a single TES measurement is dependent on the atmospheric and surface conditions as well as on the instrument noise. For observations where the cloud optical depth is less than 0.5, we find that the TES detection limit for PAN is in the region of 200 to 300 pptv. We show that PAN retrievals capture plumes associated with boreal burning. Retrievals over the Northern Hemisphere Pacific in springtime show spatial features that are qualitatively consistent with the expected distribution of PAN in outflow of Asian pollution.

Ozone is a tropospheric pollutant and plays a key role in determining the air quality that affects human wellbeing. In this study, we compare the capability of two hypothetical grating spectrometers onboard a geostationary (GEO) satellite to sense ozone in the lowermost troposphere (surface and the 0–1 km column). We consider one week during the Northern Hemisphere summer simulated by a chemical transport model, and use the two GEO instrument configurations to measure ozone concentration (1) in the thermal infrared (GEO TIR) and (2) in the thermal infrared and the visible (GEO TIR+VIS). These configurations are compared against each other, and also against an ozone reference state and a priori ozone information. In a first approximation, we assume clear sky conditions neglecting the influence of aerosols and clouds. A number of statistical tests are used to assess the performance of the two GEO configurations. We consider land and sea pixels and whether differences between the two in the performance are significant. Results show that the GEO TIR+VIS configuration provides a better representation of the ozone field both for surface ozone and the 0–1 km ozone column during the daytime especially over land.

We apply the Tropospheric Emission Spectrometer (TES) ozone retrieval algorithm to Infrared Atmospheric Sounding Instrument (IASI) radiances and characterise the uncertainties and information content of the retrieved ozone profiles. This study focuses on mid-latitudes for the year 2008. We validate our results by comparing the IASI ozone profiles to ozone sondes. In the sonde comparisons, we find a negative bias (1-10%) in the IASI profiles in the lower to mid-troposphere and a positive bias (up to 14%) in the upper troposphere/lower stratosphere (UTLS) region. For the described cases, the degrees of freedom for signal are on average 3.2, 0.3, 0.8, and 0.9 for the columns 0 km - top of atmosphere, (0-6), (0-11), and (8-16) km, respectively. We find that our biases with respect to sondes and our degrees of freedom for signal for ozone are comparable to previously published results from other IASI ozone algorithms. In addition to evaluating biases, we validate the retrieval errors by comparing predicted errors to the sample covariance matrix of the IASI observations themselves. For the predicted versus empirical error comparison, we find that these errors are consistent and that the measurement noise and the interference of temperature and water vapour on the retrieval together mostly explain the empirically derived random errors. In general, the precision of the IASI ozone profiles is better than 20%., SCOPUS: ar.j, info:eu-repo/semantics/published

We present a description of the Tropospheric Emission Spectrometer (TES) carbonyl sulfide (OCS) retrieval algorithm, along with evaluation of the biases and uncertainties against aircraft profiles from the HIPPO campaign and data from the NOAA Mauna Loa site. In general, the OCS retrievals (1) have less than 1.0 degree of freedom for signals (DOFs), (2) are sensitive in the mid-troposphere with a peak sensitivity typically between 300 to 500 hPa, (3) but have much smaller systematic errors from temperature, CO2 and H2O calibrations relative to random errors from measurement noise. Here we estimate the monthly means from TES measurements averaged over multiple years so that random errors are reduced and useful information about OCS seasonal and latitudinal variability can be derived. With this averaging, TES OCS data are found to be consistent (within the calculated uncertainties) with NOAA ground observations and HIPPO aircraft measurements. TES OCS data also captures the seasonal and latitudinal variations observed by these in situ data.

A current obstacle to the Observation System Simulation Experiments (OSSEs) used to quantify the potential performance of future atmospheric composition remote sensing systems is a computationally efficient method to define the scene-dependent vertical sensitivity of measurements as expressed by the retrieval averaging kernels (AKs). We present a method for the efficient prediction of AKs for multispectral retrievals of carbon monoxide (CO) and ozone (O3) based on actual retrievals from MOPITT on EOS-Terra and TES and OMI on EOS-Aura, respectively. This employs a multiple regression approach for deriving scene-dependent AKs using predictors based on state parameters such as the thermal contrast between the surface and lower atmospheric layers, trace gas volume mixing ratios (VMR), solar zenith angle, water vapor amount, etc. We first compute the singular vector decomposition (SVD) for individual cloud-free AKs and retain the 1st three ranked singular vectors in order to fit the most significant, orthogonal components of the AK in the subsequent multiple regression on a training set of retrieval cases. The resulting fit coefficients are applied to the predictors from a different test set of retrievals cased to reconstruct predicted AKs, which can then be evaluated against the true test set retrieval AKs. By comparing the VMR profile adjustment resulting from the use of the predicted vs. true AKs, we quantify the CO and O3 VMR profile errors associated with the use of the predicted AKs compared to the true AKs that might be obtained from a computationally expensive full retrieval calculation as part of an OSSE. Similarly, we estimate the errors in CO and O3 VMRs from using a single regional average AK to represent all retrievals, which has been a common approximation in chemical OSSEs performed to-date. For both CO and O3 in the lower troposphere, we find a significant reduction in error when using the predicted AKs as compared to a single average AK. This study examined data from the continental United States (CONUS) for 2006, but the approach could be applied to other regions and times.

Tropical fires represent a highly uncertain source of atmospheric methane (CH4) because of the variability of fire emissions and the dependency of the fire CH4 emission factors (g kg−1 dry matter burned) on fuel type and combustion phase. In this paper we use new observations of CH4 and CO in the free troposphere from the Aura Tropospheric Emission Sounder (TES) satellite instrument to place constraints on the role of tropical fire emissions versus microbial production (e.g. in wetlands and livestock) during the (October) 2006 El Nino, a time of significant peat fire emissions from Indonesia We first evaluate the global CH4 distributions from TES using the GEOS-Chem model. We find a mean bias between the observations and model of 26.3 ppb CH4 that is independent of latitude between 50° S and 80° N consistent with previous validation studies of TES CH4 retrievals using aircraft measurements. The slope of the distribution of CH4 versus CO as observed by TES and modeled by GEOS-Chem is consistent (within the TES observation error) for air parcels over the Indonesian peat fires, South America, and Africa. The CH4 and CO distributions are correlated between R = 0.42 and R = 0.46, with these correlations primarily limited by the TES random error. Over Indonesia, the observed slope of 0.13 (ppb ppb−1) ± 0.01, as compared to a modeled slop of 0.153 (ppb ppb−1) ± 0.005 and an emission ratio used within the GEOS-Chem model of approximately 0.11 (ppb ppb−1) indicates that most of the observed methane enhancement originated from the fire. Slopes of 0.47 (ppb ppb−1) ± 0.04 and 0.44 (ppb ppb−1) ± 0.03 over South America and Africa show that the methane in the observed air parcels primarily came from microbial generated emissions. Sensitivity studies using GEOS-Chem show that part of the observed correlation for the Indonesian observations and most of the observed correlations over South America and Africa are a result of transport and mixing of the fire and nearby microbial generated emissions into the observed air parcels. Differences between observed and modeled CH4 distributions over South America and Southern Africa indicate that the magnitude of the methane emissions for this time period are inconsistent with observations even if the relative distribution of fire versus biotic emissions are consistent. This study shows the potential for estimation of CH4 emissions over tropical regions using joint satellite observations of CH4 and CO.

We use simultaneous observations of ozone and outgoing longwave radiation (OLR) from the Tropospheric Emission Spectrometer (TES) to evaluate ozone distributions and radiative forcing simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES for 2005–2010. These biases lead to substantial differences in ozone instantaneous radiative forcing between TES and the ACCMIP simulations. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean has a low bias in the SH tropics of up to 100 m W m−2 locally and a global low bias of 35 ± 44 m W m−2 relative to TES. Combining ACCMIP preindustrial ozone and the TES present-day ozone, we calculate an observationally constrained estimate of tropospheric ozone radiative forcing (RF) of 399 ± 70 m W m−2, which is about 7% higher than using the ACCMIP models alone but with the same standard deviation (Stevenson et al., 2012). In addition, we explore an alternate approach to constraining radiative forcing estimates by choosing a subset of models that best match TES ozone, which leads to an ozone RF of 369 ± 42 m W m−2. This estimate is closer to the ACCMIP ensemble mean RF but about a 40% reduction in standard deviation. These results point towards a profitable direction of combining observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

[1] We calculate the sensitivity of outgoing longwave radiation (OLR) to the global vertical distribution of tropospheric ozone using ozone profile estimates from Aura Tropospheric Emission Spectrometer (Aura-TES) along with the partial derivatives of spectral radiance with respect to ozone from the Aura-TES operational retrieval algorithm. Accounting for anisotropy, we calculate top of atmosphere instantaneous radiative kernels (IRKs), in W/m2/ppb, for infrared ozone absorption from 985 to 1080 cm−1. Zonal mean distributions for August 2006 show significant variations in the IRK between clear and cloudy sky, ocean and land, and day and night over land. For all sky (clear and cloudy conditions), OLR is significantly less sensitive to ozone in the middle and lower troposphere due to clouds, especially in the tropics. We also compute the longwave radiative effect (LWRE), i.e., the reduction in OLR due to absorption by tropospheric ozone, and find a global average LWRE of 0.33 ± 0.02−0.007+0.018 W/m2 (with uncertainty and bias) for tropospheric ozone with significant variability (σ = 0.23W/m2) under all sky conditions for August 2006. For clear sky, tropical conditions we examine the effect of water vapor in reducing the LWRE from tropospheric ozone.

[1] We examine the photochemical processes governing the production of ozone in smoke from large Siberian fires that formed in July 2006 using colocated O 3 and CO profiles as measured by the Tropospheric Emission Spectrometer as well as NO 2 and aerosol optical depths as measured by the Ozone Monitoring Instrument. The Real-Time Air Quality Model (RAQMS) is used to explain the observed variations of O 3 . Enhanced levels of ozone up to 90 parts per billion (ppbv) are observed near and away from the Siberian fires (60°N and 100°E) when sunlight and NO x are available. We also observe significantly low O 3 amounts (less then 30 ppbv) in the smoke plume from Siberian fires in conjunction with optically thick aerosols. Despite this wide variance in observed ozone values, the mean ozone value for all observations of the smoke plume is close to background levels of approximately 55 ppbv in the free troposphere. Using RAQMS we show that optically thick aerosols in the smoke plume can substantially reduce the photochemical production of ozone and this can explain why the observed mean ozone amount for all plume observations is not much larger than background values of 55 ppbv. However, the anonymously low ozone amounts of 30 ppbv or less point toward other unresolved processes that reduce ozone below background levels in the plume.